Subspace Estimation with Automatic Dimension and Variable Selection in Sufficient Dimension Reduction

Jing Zeng, Qing Mai & Xin Zhang
Sufficient dimension reduction (SDR) methods target finding lower-dimensional representations of a multivariate predictor to preserve all the information about the conditional distribution of the response given the predictor. The reduction is commonly achieved by projecting the predictor onto a low-dimensional subspace. The smallest such subspace is known as the Central Subspace (CS) and is the key parameter of interest for most SDR methods. In this article, we propose a unified and flexible framework for estimating...
1 citation reported since publication in 2022.
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